Human Disease Variation in the Light of Population Genomics

Population-enriched innate immune variants may identify candidate gene targets at the intersection of cancer and cardio-metabolic disease

Both cancer and cardio-metabolic disease disparities exist among specific populations in the US. For example, African Americans experience the highest rates of breast and prostate cancer mortality and the highest incidence of obesity. Native and Hispanic Americans experience the highest rates of liver cancer mortality. At the same time, Pacific Islanders have the highest death rate attributed to type 2 diabetes (T2D), and Asian Americans experience the highest incidence of non-alcoholic fatty liver disease (NAFLD) and cancers induced by infectious agents. Notably, the pathologic progression of both cancer and cardio-metabolic diseases involves innate immunity and mechanisms of inflammation. Innate immunity in individuals is established through genetic inheritance and external stimuli to respond to environmental threats and stresses such as pathogen exposure. Further, individual genomes contain characteristic genetic markers associated with one or more geographic ancestries (ethnic groups), including protective innate immune genetic programming optimized for survival in their corresponding ancestral environment(s). This perspective explores evidence related to our working hypothesis that genetic variations in innate immune genes, particularly those that are commonly found but unevenly distributed between populations, are associated with disparities between populations in both cancer and cardio-metabolic diseases. Identifying conventional and unconventional innate immune genes that fit this profile may provide critical insights into the underlying mechanisms that connect these two families of complex diseases and offer novel targets for precision-based treatment of cancer and/or cardio-metabolic disease.

PopTradeOff: A database for exploring population-specificity of adaptive evolution, disease susceptibility, and drug responsiveness

The influence of adaptive evolution on disease susceptibility has drawn attention; however, the extent of the influence, whether favored mutations also influence drug responses, and whether the associations between the three are population-specific remain unknown. Using a reported deep learning network to integrate seven statistical tests for detecting selection signals, we predicted favored mutations in the genomes of 17 human populations and integrated these favored mutations with reported GWAS sites and drug response-related variants into the database PopTradeOff (http://www.gaemons.net/PopFMIntro). The database also contains genome annotation information on the SNP, sequence, gene, and pathway levels. The preliminary data analyses suggest that substantial associations exist between adaptive evolution, disease susceptibility, and drug responses and that the associations are highly population-specific. The database may be valuable for disease studies, drug development, and personalized medicine.

Nutrigenomics in the context of evolution

Nutrigenomics describes the interaction between nutrients and our genome. Since the origin of our species most of these nutrient-gene communication pathways have not changed. However, our genome experienced over the past 50,000 years a number of evolutionary pressures, which are based on the migration to new environments concerning geography and climate, the transition from hunter-gatherers to farmers including the zoonotic transfer of many pathogenic microbes and the rather recent change of societies to a preferentially sedentary lifestyle and the dominance of Western diet. Human populations responded to these challenges not only by specific anthropometric adaptations, such as skin color and body stature, but also through diversity in dietary intake and different resistance to complex diseases like the metabolic syndrome, cancer and immune disorders. The genetic basis of this adaptation process has been investigated by whole genome genotyping and sequencing including that of DNA extracted from ancient bones. In addition to genomic changes, also the programming of epigenomes in pre- and postnatal phases of life has an important contribution to the response to environmental changes. Thus, insight into the variation of our (epi)genome in the context of our individual's risk for developing complex diseases, helps to understand the evolutionary basis how and why we become ill. This review will discuss the relation of diet, modern environment and our (epi)genome including aspects of redox biology. This has numerous implications for the interpretation of the risks for disease and their prevention.

Genome-wide association studies of COVID-19: Connecting the dots

Genome-wide association studies (GWASs) are a research approach used to identify genetic variants associated with common diseases, like COVID-19. The lead genetic variants (n = 41) reported by the eleven largest COVID-19 GWASs are mapped to 22 different chromosomal regions. The loci 3q21.31 (LZTFL1 and chemokine receptor genes) and 9q34.2 (ABO), associated with disease severity and susceptibility to infection, respectively, were the most replicated findings across studies. Genes involved with mucociliary clearance (CEP97, FOXP4), viral-entry (ACE2, SLC6A20) and mucosal immunity (MIR6891) are associated with the risk of SARS-CoV-2 infection while genes of antiviral immune response (IFNAR2, OAS1), leukocyte trafficking (CCR9, CXCR6) and lung injury (DPP9, NOTCH4) are associated with severe disease. The biological processes underlying the risk of infection occur prominently, but not exclusively, in the upper airways whereas the severe COVID-19-associated processes in alveolar-capillary interface. The COVID-19 GWASs has unraveled key genetic mechanisms of SARS-CoV-2 pathogenesis, although the genetic basis of other COVID-19 related phenotypes (long COVID and neurological impairment) remains to be elucidated.

Identification of Key Genes and Pathways Associated with Preeclampsia by a WGCNA and an Evolutionary Approach

Preeclampsia (PE) is the serious obstetric-related disease characterized by newly onset hypertension and causes damage to the kidneys, brain, liver, and more. To investigate genes with key roles in PE’s pathogenesis and their contributions, we used a microarray dataset of normotensive and PE patients and conducted a weighted gene co-expression network analysis (WGCNA). Cyan and magenta modules that are highly enriched with differentially expressed genes (DEGs) were revealed. By using the molecular complex detection (MCODE) algorithm, we identified five significant clusters in the cyan module protein–protein interaction (PPI) network and nine significant clusters in the magenta module PPI network. Our analyses indicated that (i) human accelerated region (HAR) genes are enriched in the magenta-associated C6 cluster, and (ii) positive selection (PS) genes are enriched in the cyan-associated C3 and C5 clusters. We propose these enriched HAR and PS genes, i.e., EIF4E, EIF5, EIF3M, DDX17, SRSF11, PSPC1, SUMO1, CAPZA1, PSMD14, and MNAT1, including highly connected hub genes, HNRNPA1, RBMX, PRKDC, and RANBP2, as candidate key genes for PE’s pathogenesis. A further clarification of the functions of these PPI clusters and key enriched genes will contribute to the discovery of diagnostic biomarkers for PE and therapeutic intervention targets.

Relevance of CYP2D6 Gene Variants in Population Genetic Differentiation

A significant portion of the variability in complex features, such as drug response, is likely caused by human genetic diversity. One of the highly polymorphic pharmacogenes is CYP2D6, encoding an enzyme involved in the metabolism of about 25% of commonly prescribed drugs. In a directed search of the 1000 Genomes Phase III variation data, 86 single nucleotide polymorphisms (SNPs) in the CYP2D6 gene were extracted from the genotypes of 2504 individuals from 26 populations, and then used to reconstruct haplotypes. Analyses were performed using Haploview, Phase, and Arlequin softwares. Haplotype and nucleotide diversity were high in all populations, but highest in populations of African ancestry. Pairwise F_ST showed significant results for eleven SNPs, six of which were characteristic of African populations, while four SNPs were most common in East Asian populations. A principal component analysis of CYP2D6 haplotypes showed that African populations form one cluster, Asian populations form another cluster with East and South Asian populations separated, while European populations form the third cluster. Linkage disequilibrium showed that all African populations have three or more haplotype blocks within the CYP2D6 gene, while other world populations have one, except for Chinese Dai and Punjabi in Pakistan populations, which have two.

Ancestry-related distribution of Runs of homozygosity and functional variants in Qatari population

Background

Describing how genetic history shapes the pattern of medically relevant variants could improve the understanding of how specific loci interact with each other and affect diseases and traits prevalence. The Qatari population is characterized by a complex history of admixture and substructure, and the study of its population genomic features would provide valuable insights into the genetic landscape of functional variants. Here, we analyzed the genomic variation of 186 newly-genotyped healthy individuals from the Qatari peninsula.

Results

We discovered an intricate genetic structure using ancestry related analyses. In particular, the presence of three different clusters, Cluster 1, Cluster 2 and Cluster 3 (with Near Eastern, South Asian and African ancestry, respectively), was detected with an additional fourth one (Cluster 4) with East Asian ancestry. These subpopulations show differences in the distribution of runs of homozygosity (ROH) and admixture events in the past, ranging from 40 to 5 generations ago. This complex genetic history led to a peculiar pattern of functional markers under positive selection, differentiated in shared signals and private signals. Interestingly we found several signatures of shared selection on SNPs in the FADS2 gene, hinting at a possible common evolutionary link to dietary intake. Among the private signals, we found enrichment for markers associated with HDL and LDL for Cluster 1(Near Eastern ancestry) and Cluster 3 (South Asian ancestry) and height and blood traits for Cluster 2 (African ancestry).

The differences in genetic history among these populations also resulted in the different frequency distribution of putative loss of function variants. For example, homozygous carriers for rs2884737, a variant linked to an anticoagulant drug (warfarin) response, are mainly represented by individuals with predominant Bedouin ancestry (risk allele frequency G at 0.48).

Conclusions

We provided a detailed catalogue of the different ancestral pattern in the Qatari population highlighting differences and similarities in the distribution of selected variants and putative loss of functions. Finally, these results would provide useful guidance for assessing genetic risk factors linked to consanguinity and genetic ancestry.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12863-022-01087-1.

Uncovering the extensive trade-off between adaptive evolution and disease susceptibility

Favored mutations in the human genome may make the carriers adapt to changing environments and lifestyles but also susceptible to specific diseases. The scale and details of the trade-off between adaptive evolution and disease susceptibility are unclear because most favored mutations in different populations remain unidentified. As no statistical test can discriminate favored mutations from nearby hitchhiking neutral ones, we report a deep-learning network (DeepFavored) to integrate multiple statistical tests and divide identifying favored mutations into two subtasks. We identify favored mutations in three human populations and analyzed the correlation between favored/hitchhiking mutations and genome-wide association study (GWAS) sites. Both favored and hitchhiking neutral mutations are enriched in GWAS sites with population-specific features, and the enrichment and population specificity are prominent in genes in specific Gene Ontology (GO) terms. These provide evidence for extensive and population-specific trade-offs between adaptive evolution and disease susceptibility. The unveiled scale helps understand and investigate differences and diseases of humans.

Genetic diversity in Kashubs: the regional increase in the frequency of several disease-causing variants

Differential distribution of genetic variants’ frequency among human populations is caused by the genetic drift in isolated populations, historical migrations, and demography. Some of these variants are identical by descent and represent founder mutations, which — if pathogenic in nature — lead to the increased frequency of otherwise rare diseases. The detection of the increased regional prevalence of pathogenic variants may shed light on the historical processes that affected studied populations and can help to develop effective screening and diagnostic strategies as a part of personalized medicine. Here, we discuss the specific genetic diversity in Kashubs, the minority group living in northern Poland, reflected in the biased distribution of some of the repetitively found disease-causing variants. These include the following: (1) c.662A > G (p.Asp221Gly) in LDLR, causing heterozygous familial hypercholesterolemia; (2) c.3700_3704del in BRCA1, associated with hereditary breast and ovarian cancer syndrome; (3) c.1528G > C (p.Glu510Gln) in HADHA, seen in long-chain 3-hydroxy acyl-CoA dehydrogenase (LCHAD) deficiency, and (4) c.1032delT in NPHS2, associated with steroid-resistant nephrotic syndrome.

Process-explicit models reveal the structure and dynamics of biodiversity patterns

With ever-growing data availability and computational power at our disposal, we now have the capacity to use process-explicit models more widely to reveal the ecological and evolutionary mechanisms responsible for spatiotemporal patterns of biodiversity. Most research questions focused on the distribution of diversity cannot be answered experimentally, because many important environmental drivers and biological constraints operate at large spatiotemporal scales. However, we can encode proposed mechanisms into models, observe the patterns they produce in virtual environments, and validate these patterns against real-world data or theoretical expectations. This approach can advance understanding of generalizable mechanisms responsible for the distributions of organisms, communities, and ecosystems in space and time, advancing basic and applied science. We review recent developments in process-explicit models and how they have improved knowledge of the distribution and dynamics of life on Earth, enabling biodiversity to be better understood and managed through a deeper recognition of the processes that shape genetic, species, and ecosystem diversity.

Recombination affects allele-specific expression of deleterious variants in human populations

How the genetic composition of a population changes through stochastic processes, such as genetic drift, in combination with deterministic processes, such as selection, is critical to understanding how phenotypes vary in space and time. Here, we show how evolutionary forces affecting selection, including recombination and effective population size, drive genomic patterns of allele-specific expression (ASE). Integrating tissue-specific genotypic and transcriptomic data from 1500 individuals from two different cohorts, we demonstrate that ASE is less often observed in regions of low recombination, and loci in high or normal recombination regions are more efficient at using ASE to underexpress harmful mutations. By tracking genetic ancestry, we discriminate between ASE variability due to past demographic effects, including subsequent bottlenecks, versus local environment. We observe that ASE is not randomly distributed along the genome and that population parameters influencing the efficacy of natural selection alter ASE levels genome wide.

Globally Relaxed Selection and Local Adaptation in Boechera stricta

The strength of selection varies among populations and across the genome, but the determinants of efficacy of selection remain unclear. In this study, we used whole-genome sequencing data from 467 Boechera stricta accessions to quantify the strength of selection and characterize the pattern of local adaptation. We found low genetic diversity on 0-fold degenerate sites and conserved non-coding sites, indicating functional constraints on these regions. The estimated distribution of fitness effects and the proportion of fixed substitutions suggest relaxed negative and positive selection in B. stricta. Among the four population groups, the NOR and WES groups have smaller effective population size (Ne), higher proportions of effectively neutral sites, and lower rates of adaptive evolution compared with UTA and COL groups, reflecting the effect of Ne on the efficacy of natural selection. We also found weaker selection on GC-biased sites compared with GC-conservative (unbiased) sites, suggested that GC-biased gene conversion has affected the strength of selection in B. stricta. We found mixed evidence for the role of the recombination rate on the efficacy of selection. The positive and negative selection was stronger in high-recombination regions compared with low-recombination regions in COL but not in other groups. By scanning the genome, we found different subsets of selected genes suggesting differential adaptation among B. stricta groups. These results show that differences in effective population size, nucleotide composition, and recombination rate are important determinants of the efficacy of selection. This study enriches our understanding of the roles of natural selection and local adaptation in shaping genomic variation.

Leveraging Northern European population history: novel low-frequency variants for polycystic ovary syndrome

STUDY QUESTION

Can we identify novel variants associated with polycystic ovary syndrome (PCOS) by leveraging the unique population history of Northern Europe?

SUMMARY ANSWER

We identified three novel genome-wide significant associations with PCOS, with two putative independent causal variants in the checkpoint kinase 2 (CHEK2) gene and a third in myosin X (MYO10).

WHAT IS KNOWN ALREADY

PCOS is a common, complex disorder with unknown aetiology. While previous genome-wide association studies (GWAS) have mapped several loci associated with PCOS, the analysis of populations with unique population history and genetic makeup has the potential to uncover new low-frequency variants with larger effects.

STUDY DESIGN, SIZE, DURATION

A population-based case-control GWAS was carried out.

PARTICIPANTS/MATERIALS, SETTING, METHODS

We identified PCOS cases from national registers by ICD codes (ICD-10 E28.2, ICD-9 256.4, or ICD-8 256.90), and all remaining women were considered controls. We then conducted a three-stage case-control GWAS: in the discovery phase, we had a total of 797 cases and 140 558 controls from the FinnGen study. For validation, we used an independent dataset from the Estonian Biobank, including 2812 cases and 89 230 controls. Finally, we performed a joint meta-analysis of 3609 cases and 229 788 controls from both cohorts. Additionally, we reran the association analyses including BMI as a covariate, with 2169 cases and 160 321 controls from both cohorts.

MAIN RESULTS AND THE ROLE OF CHANCE

Two out of the three novel genome-wide significant variants associating with PCOS, rs145598156 (P = 3.6×10-8, odds ratio (OR) = 3.01 [2.02-4.50] minor allele frequency (MAF) = 0.005) and rs182075939 (P = 1.9×10-16, OR = 1.69 [1.49-1.91], MAF = 0.04), were found to be enriched in the Finnish and Estonian populations and are tightly linked to a deletion c.1100delC (r2 = 0.95) and a missense I157T (r2 = 0.83) in CHEK2. The third novel association is a common variant near MYO10 (rs9312937, P = 1.7 × 10-8, OR = 1.16 [1.10-1.23], MAF = 0.44). We also replicated four previous reported associations near the genes Erb-B2 Receptor Tyrosine Kinase 4 (ERBB4), DENN Domain Containing 1A (DENND1A), FSH Subunit Beta (FSHB) and Zinc Finger And BTB Domain Containing 16 (ZBTB16). When adding BMI as a covariate only one of the novel variants remained genome-wide significant in the meta-analysis (the EstBB lead signal in CHEK2 rs182075939, P = 1.9×10-16, OR = 1.74 [1.5-2.01]) possibly owing to reduced sample size.

LARGE SCALE DATA

The age- and BMI-adjusted GWAS meta-analysis summary statistics are available for download from the GWAS Catalog with accession numbers GCST90044902 and GCST90044903.

LIMITATIONS, REASONS FOR CAUTION

The main limitation was the low prevalence of PCOS in registers; however, the ones with the diagnosis most likely represent the most severe cases. Also, BMI data were not available for all (63% for FinnGen, 76% for EstBB), and the biobank setting limited the accessibility of PCOS phenotypes and laboratory values.

WIDER IMPLICATIONS OF THE FINDINGS

This study encourages the use of isolated populations to perform genetic association studies for the identification of rare variants contributing to the genetic landscape of complex diseases such as PCOS.

STUDY FUNDING/COMPETING INTEREST(S)

This work has received funding from the European Union's Horizon 2020 research and innovation programme under the MATER Marie Skłodowska-Curie grant agreement No. 813707 (N.P.-G., T.L., T.P.), the Estonian Research Council grant (PRG687, T.L.), the Academy of Finland grants 315921 (T.P.), 321763 (T.P.), 297338 (J.K.), 307247 (J.K.), 344695 (H.L.), Novo Nordisk Foundation grant NNF17OC0026062 (J.K.), the Sigrid Juselius Foundation project grants (T.L., J.K., T.P.), Finska Läkaresällskapet (H.L.) and Jane and Aatos Erkko Foundation (H.L.). The funders had no role in study design, data collection and analysis, publishing or preparation of the manuscript. The authors declare no conflicts of interest.

Variation among human populations in endometriosis and PCOS A test of the inverse comorbidity model

Evidence linking endometriosis to low prenatal testosterone, and evidence that risk of polycystic ovary syndrome (PCOS) is associated with high prenatal testosterone, have motivated the hypothesis that endometriosis and PCOS exhibit inverse comorbidity. The inverse comorbidity hypothesis predicts that populations exhibiting higher prevalence of one disorder should show lower prevalence of the other. To test this prediction, data were compiled from the literature on the prevalence of endometriosis and PCOS, levels of serum testosterone in women during pregnancy and digit ratios as indicators of prenatal testosterone, in relation to variation in inferred or observed population ancestries. Published studies indicate that rates of endometriosis are highest in women from Asian populations, intermediate in women from European populations and lowest in women from African populations (i.e. with inferred or observed African ancestry); by contrast, rates of PCOS show evidence of being lowest in Asian women, intermediate in Europeans and highest in individuals from African populations. Women from African populations also show higher serum testosterone during pregnancy (which may increase PCOS risk, and decrease endometriosis risk, in daughters), and higher prenatal testosterone (as indicated by digit ratios), than European women. These results are subject to caveats involving ascertainment biases, socioeconomic, cultural and historical effects on diagnoses, data quality, uncertainties regarding the genetic and environmental bases of population differences and population variation in the causes and symptoms of PCOS and endometriosis. Despite such reservations, the findings provide convergent, preliminary support for the inverse comorbidity model, and they should motivate further tests of its predictions.

Lay Summary: Given that endometriosis risk and risk of polycystic ovary syndrome show evidence of having genetically, developmentally, and physiologically opposite causes, they should also show opposite patterns of prevalence within populations: where one is more common, the other should be more rare. This hypothesis is supported by data from studies of variation among populations in rates of endometriosis and PCOS and studies of variation among populations in levels of prenatal testosterone, which mediaterisks of both conditions.

Continental-scale genomic analysis suggests shared post-admixture adaptation in the Americas

American populations are one of the most interesting examples of recently admixed groups, where ancestral components from three major continental human groups (Africans, Eurasians and Native Americans) have admixed within the last 15 generations. Recently, several genetic surveys focusing on thousands of individuals shed light on the geography, chronology and relevance of these events. However, even though gene flow could drive adaptive evolution, it is unclear whether and how natural selection acted on the resulting genetic variation in the Americas.

In this study, we analysed the patterns of local ancestry of genomic fragments in genome-wide data for ~ 6000 admixed individuals from 10 American countries. In doing so, we identified regions characterized by a divergent ancestry profile (DAP), in which a significant over or under ancestral representation is evident.

Our results highlighted a series of genomic regions with DAPs associated with immune system response and relevant medical traits, with the longest DAP region encompassing the human leukocyte antigen locus. Furthermore, we found that DAP regions are enriched in genes linked to cancer-related traits and autoimmune diseases. Then, analysing the biological impact of these regions, we showed that natural selection could have acted preferentially towards variants located in coding and non-coding transcripts and characterized by a high deleteriousness score.

Taken together, our analyses suggest that shared patterns of post admixture adaptation occurred at a continental scale in the Americas, affecting more often functional and impactful genomic variants.

Population-specific genetic background for the OPRM1 variant rs1799971 (118A>G): implications for genomic medicine and functional analysis

The mu-opioid receptor (MOR, OPRM1) has important roles in diverse functions including reward, addiction, and response to pain treatment. SNP rs1799971 (118A > G, N40D) which occur at a high frequency (40-60%) in Asia and moderate frequency (15%) in samples of European ancestry, is the only common coding variant in the canonical transcript, in non-African populations. Despite extensive studies, the molecular consequences of this variation remained unresolved. The aim of this study was to determine the genetic background of the OPRM1 region of 118G in four representative populations and to assess its potential modulatory effect. Seven common haplotypes with distinct population distribution were identified based on seven SNPs. Three haplotypes carry the 118G and additional highly linked regulatory SNPs (e.g., rs9383689) that could modulate the effect of 118G. Extended analysis in the 1000 Genomes database (n = 2504) revealed a common East Asian-specific haplotype with a different genetic background in which there are no variant alleles for an upstream LD block tagged by the eQTL rs9397171. The major European haplotype specifically includes the eQTL intronic SNP rs62436463 that must have arisen after the split between European and Asian populations. Differentiating between the effect of 118G and these SNPs requires specific experimental approaches. The analysis also revealed a significant increase in two 118A haplotypes with eQTL SNPs associated with drug addiction (rs510769) and obesity (rs9478496) in populations with native Mexican ancestry. Future studies are required to assess the clinical implication of these findings.

Peopling of the Americas as inferred from ancient genomics

In less than a decade, analyses of ancient genomes have transformed our understanding of the Indigenous peopling and population history of the Americas. These studies have shown that this history, which began in the late Pleistocene epoch and continued episodically into the Holocene epoch, was far more complex than previously thought. It is now evident that the initial dispersal involved the movement from northeast Asia of distinct and previously unknown populations, including some for whom there are no currently known descendants. The first peoples, once south of the continental ice sheets, spread widely, expanded rapidly and branched into multiple populations. Their descendants-over the next fifteen millennia-experienced varying degrees of isolation, admixture, continuity and replacement, and their genomes help to illuminate the relationships among major subgroups of Native American populations. Notably, all ancient individuals in the Americas, save for later-arriving Arctic peoples, are more closely related to contemporary Indigenous American individuals than to any other population elsewhere, which challenges the claim-which is based on anatomical evidence-that there was an early, non-Native American population in the Americas. Here we review the patterns revealed by ancient genomics that help to shed light on the past peoples who created the archaeological landscape, and together lead to deeper insights into the population and cultural history of the Americas.

How to design a national genomic project-a systematic review of active projects

An increasing number of countries are investing efforts to exploit the human genome, in order to improve genetic diagnostics and to pave the way for the integration of precision medicine into health systems. The expected benefits include improved understanding of normal and pathological genomic variation, shorter time-to-diagnosis, cost-effective diagnostics, targeted prevention and treatment, and research advances.We review the 41 currently active individual national projects concerning their aims and scope, the number and age structure of included subjects, funding, data sharing goals and methods, and linkage with biobanks, medical data, and non-medical data (exposome). The main aims of ongoing projects were to determine normal genomic variation (90%), determine pathological genomic variation (rare disease, complex diseases, cancer, etc.) (71%), improve infrastructure (59%), and enable personalized medicine (37%). Numbers of subjects to be sequenced ranges substantially, from a hundred to over a million, representing in some cases a significant portion of the population. Approximately half of the projects report public funding, with the rest having various mixed or private funding arrangements. 90% of projects report data sharing (public, academic, and/or commercial with various levels of access) and plan on linking genomic data and medical data (78%), existing biobanks (44%), and/or non-medical data (24%) as the basis for enabling personal/precision medicine in the future.Our results show substantial diversity in the analysed categories of 41 ongoing national projects. The overview of current designs will hopefully inform national initiatives in designing new genomic projects and contribute to standardisation and international collaboration.

Novel Risk Loci Associated With Genetic Risk for Bipolar Disorder Among Han Chinese Individuals: A Genome-Wide Association Study and Meta-analysis

IMPORTANCE

The genetic basis of bipolar disorder (BD) in Han Chinese individuals is not fully understood.

OBJECTIVE

To explore the genetic basis of BD in the Han Chinese population.

DESIGN, SETTING, AND PARTICIPANTS

A genome-wide association study (GWAS), followed by independent replication, was conducted to identify BD risk loci in Han Chinese individuals. Individuals with BD were diagnosed based on DSM-IV criteria and had no history of schizophrenia, mental retardation, or substance dependence; individuals without any personal or family history of mental illnesses, including BD, were included as control participants. In total, discovery samples from 1822 patients and 4650 control participants passed quality control for the GWAS analysis. Replication analyses of samples from 958 patients and 2050 control participants were conducted. Summary statistics from the European Psychiatric Genomics Consortium 2 (PGC2) BD GWAS (20 352 cases and 31 358 controls) were used for the trans-ancestry genetic correlation analysis, polygenetic risk score analysis, and meta-analysis to compare BD genetic risk between Han Chinese and European individuals. The study was performed in February 2020.

MAIN OUTCOMES AND MEASURES

Single-nucleotide variations with P < 5.00 × 10-8 were considered to show genome-wide significance of statistical association.

RESULTS

The Han Chinese discovery GWAS sample included 1822 cases (mean [SD] age, 35.43 [14.12] years; 838 [46%] male) and 4650 controls (mean [SD] age, 27.48 [5.97] years; 2465 [53%] male), and the replication sample included 958 cases (mean [SD] age, 37.82 [15.54] years; 412 [43%] male) and 2050 controls (mean [SD] age, 27.50 [6.00] years; 1189 [58%] male). A novel BD risk locus in Han Chinese individuals was found near the gene encoding transmembrane protein 108 (TMEM108, rs9863544; P = 2.49 × 10-8; odds ratio [OR], 0.650; 95% CI, 0.559-0.756), which is required for dendritic spine development and glutamatergic transmission in the dentate gyrus. Trans-ancestry genetic correlation estimation (ρge = 0.652, SE = 0.106; P = 7.30 × 10-10) and polygenetic risk score analyses (maximum liability-scaled Nagelkerke pseudo R2 = 1.27%; P = 1.30 × 10-19) showed evidence of shared BD genetic risk between Han Chinese and European populations, and meta-analysis identified 2 new GWAS risk loci near VRK2 (rs41335055; P = 4.98 × 10-9; OR, 0.849; 95% CI, 0.804-0.897) and RHEBL1 (rs7969091; P = 3.12 × 10-8; OR, 0.932; 95% CI, 0.909-0.956).

CONCLUSIONS AND RELEVANCE

This GWAS study identified several loci and genes involved in the heritable risk of BD, providing insights into its genetic architecture and biological basis.

SNP-Density Crossover Maps of Polymorphic Transposable Elements and HLA Genes Within MHC Class I Haplotype Blocks and Junction

The genomic region (~4 Mb) of the human major histocompatibility complex (MHC) on chromosome 6p21 is a prime model for the study and understanding of conserved polymorphic sequences (CPSs) and structural diversity of ancestral haplotypes (AHs)/conserved extended haplotypes (CEHs). The aim of this study was to use a set of 95 MHC genomic sequences downloaded from a publicly available BioProject database at NCBI to identify and characterise polymorphic human leukocyte antigen (HLA) class I genes and pseudogenes, MICA and MICB, and retroelement indels as haplotypic lineage markers, and single-nucleotide polymorphism (SNP) crossover loci in DNA sequence alignments of different haplotypes across the Olfactory Receptor (OR) gene region (~1.2 Mb) and the MHC class I region (~1.8 Mb) from the GPX5 to the MICB gene. Our comparative sequence analyses confirmed the identity of 12 haplotypic retroelement markers and revealed that they partitioned the HLA-A/B/C haplotypes into distinct evolutionary lineages. Crossovers between SNP-poor and SNP-rich regions defined the sequence range of haplotype blocks, and many of these crossover junctions occurred within particular transposable elements, lncRNA, OR12D2, MUC21, MUC22, PSORS1A3, HLA-C, HLA-B, and MICA. In a comparison of more than 250 paired sequence alignments, at least 38 SNP-density crossover sites were mapped across various regions from GPX5 to MICB. In a homology comparison of 16 different haplotypes, seven CEH/AH (7.1, 8.1, 18.2, 51.x, 57.1, 62.x, and 62.1) had no detectable SNP-density crossover junctions and were SNP poor across the entire ~2.8 Mb of sequence alignments. Of the analyses between different recombinant haplotypes, more than half of them had SNP crossovers within 10 kb of LTR16B/ERV3-16A3_I, MLT1, Charlie, and/or THE1 sequences and were in close vicinity to structurally polymorphic Alu and SVA insertion sites. These studies demonstrate that (1) SNP-density crossovers are associated with putative ancestral recombination sites that are widely spread across the MHC class I genomic region from at least the telomeric OR12D2 gene to the centromeric MICB gene and (2) the genomic sequences of MHC homozygous cell lines are useful for analysing haplotype blocks, ancestral haplotypic landscapes and markers, CPSs, and SNP-density crossover junctions.

Perspectives on studying molecular adaptations of amphibians in the genomic era

Understanding the genetic mechanisms underlying particular adaptations/phenotypes of organisms is one of the core issues of evolutionary biology. The use of genomic data has greatly advanced our understandings on this issue, as well as other aspects of evolutionary biology, including molecular adaptation, speciation, and even conservation of endangered species. Despite the well-recognized advantages, usages of genomic data are still limited to non-mammal vertebrate groups, partly due to the difficulties in assembling large or highly heterozygous genomes. Although this is particularly the case for amphibians, nonetheless, several comparative and population genomic analyses have shed lights into the speciation and adaptation processes of amphibians in a complex landscape, giving a promising hope for a wider application of genomics in the previously believed challenging groups of organisms. At the same time, these pioneer studies also allow us to realize numerous challenges in studying the molecular adaptations and/or phenotypic evolutionary mechanisms of amphibians. In this review, we first summarize the recent progresses in the study of adaptive evolution of amphibians based on genomic data, and then we give perspectives regarding how to effectively identify key pathways underlying the evolution of complex traits in the genomic era, as well as directions for future research.

Network and Evolutionary Analysis of Human Epigenetic Regulators to Unravel Disease Associations

We carried out a system-level analysis of epigenetic regulators (ERs) and detailed the protein–protein interaction (PPI) network characteristics of disease-associated ERs. We found that most diseases associated with ERs can be clustered into two large groups, cancer diseases and developmental diseases. ER genes formed a highly interconnected PPI subnetwork, indicating a high tendency to interact and agglomerate with one another. We used the disease module detection (DIAMOnD) algorithm to expand the PPI subnetworks into a comprehensive cancer disease ER network (CDEN) and developmental disease ER network (DDEN). Using the transcriptome from early mouse developmental stages, we identified the gene co-expression modules significantly enriched for the CDEN and DDEN gene sets, which indicated the stage-dependent roles of ER-related disease genes during early embryonic development. The evolutionary rate and phylogenetic age distribution analysis indicated that the evolution of CDEN and DDEN genes was mostly constrained, and these genes exhibited older evolutionary age. Our analysis of human polymorphism data revealed that genes belonging to DDEN and Seed-DDEN were more likely to show signs of recent positive selection in human history. This finding suggests a potential association between positive selection of ERs and risk of developmental diseases through the mechanism of antagonistic pleiotropy.

Infections of the lung: a predictive, preventive and personalized perspective through the lens of evolution, the emergence of SARS-CoV-2 and its pathogenesis

The long evolutionary battle between humans and pathogens has played an important role in shaping the current network of host-pathogen interactions. Each organ brings new challenges from the perspective of a pathogen to establish a suitable niche for survival while subverting the protective mechanisms of the host. Lungs, the organ for oxygen exchange, have been an easy target for pathogens due to its accessibility. The organ has evolved diverse capabilities to provide the flexibility required for an organism's health and at the same time maintain protective functionality to prevent and resolve assault by pathogens. The pathogenic invasions are strongly challenged by healthy lung architecture which includes the presence and activity of the epithelium, mucous, antimicrobial proteins, surfactants, and immune cells. Competitively, the pathogens in the form of viruses, bacteria, and fungi have evolved an arsenal of strategies that can over-ride the host's protective mechanisms. While bacteria such as Mycobacterium tuberculosis (M. tuberculosis) can survive in dormant form for years before getting active in humans, novel pathogens can wreak havoc as they pose a high risk of morbidity and mortality in a very short duration of time. Recently, a coronavirus strain SARS-CoV-2 has caused a pandemic which provides us an opportunity to look at the host manipulative strategies used by respiratory pathogens. Their ability to hide, modify, evade, and exploit cell's processes are key to their survival. While pathogens like M. tuberculosis have been infecting humans for thousands of years, SARS-CoV-2 has been the cause of the recent pandemic. Molecular understanding of the strategies used by these pathogens could greatly serve in design of predictive, preventive, personalized medicine (PPPM). In this article, we have emphasized on the clinically relevant evasive strategies of the pathogens in the lungs with emphasis on M. tuberculosis and SARS-CoV-2. The molecular basis of these evasive strategies illuminated through advances in genomics, cell, and structural biology can assist in the mapping of vulnerable molecular networks which can be exploited translationally. These evolutionary approaches can further assist in generating screening and therapeutic options for susceptible populations and could be a promising approach for the prediction, prevention of disease, and the development of personalized medicines. Further, tailoring the clinical data of COVID-19 patients with their physiological responses in light of known host-respiratory pathogen interactions can provide opportunities to improve patient profiling and stratification according to identified therapeutic targets.

aYChr-DB: a database of ancient human Y haplogroups

Ancient Y-Chromosomal DNA is an invaluable tool for dating and discerning the origins of migration routes and demographic processes that occurred thousands of years ago. Driven by the adoption of high-throughput sequencing and capture enrichment methods in paleogenomics, the number of published ancient genomes has nearly quadrupled within the last three years (2018-2020). Whereas ancient mtDNA haplogroup repositories are available, no similar resource exists for ancient Y-Chromosomal haplogroups. Here, we present aYChr-DB-a comprehensive collection of 1797 ancient Eurasian human Y-Chromosome haplogroups ranging from 44 930 BC to 1945 AD. We include descriptors of age, location, genomic coverage and associated archaeological cultures. We also produced a visualization of ancient Y haplogroup distribution over time. The aYChr-DB database is a valuable resource for population genomic and paleogenomic studies.

Association Between REELIN Gene Polymorphisms (rs7341475 and rs262355) and Risk of Schizophrenia: an Updated Meta-analysis

Schizophrenia (SCZ) is a destructive neuropsychiatric illness affecting millions of people worldwide. The correlation between RELN gene polymorphisms and SCZ was investigated by previous researches, though the results remained conflicting. Based on the available studies, we conducted this meta-analysis to provide a more comprehensive outcome on whether the RELN gene polymorphisms (rs7341475 and rs262355) are associated with SCZ. A total of 15 studies with 25,403 subjects (9047 cases and 16,356 controls) retrieved from PubMed, ScienceDirect, EMBASE, Wiley, BMC, Cochrane, Springer, MDPI, SAGE, and Google Scholar up to June 2020 were included. Meta-analysis was performed using Review Manager 5.3. The heterogeneity was checked using I² statistics and Q-test, whereas publication bias was also measured. The rs7341475 polymorphism showed a significantly lower risk for SCZ for the allele (A vs. G: OR = 0.93, 95%CI = 0.87-0.99), codominant 1 (AG vs. GG: OR = 0.92, 95%CI = 0.85-0.99), dominant model (AA+AG vs. GG: OR = 0.92, 95%CI = 0.86-0.98), and over dominant model (AG vs. AA+GG: OR = 0.92, 95%Cl = 0.86-0.99). The allele, codominant model 1, and dominant models remained statistically significant after the correction of the Bonferroni (p < 0.025). Subgroup analysis confirmed the association of allele and dominant models in the Caucasian after Bonferroni correction. For rs262355 polymorphism, a significantly increased risk of SCZ was found only in Caucasians for codominant 2, dominant, and allele models, but significance exists only for the allele model after Bonferroni correction. Publication bias was found in the case of codominant 2 and recessive models for rs7341475 in the overall population, but this publication was not found after performing the Bonferroni correction or after performing the subgroup analysis. No such publication was found for rs262355. The results suggest that RELN rs7341475 is associated with a lower risk of SCZ in the overall population and Caucasian population, but rs262355 is associated with an increased risk of SCZ only in the Caucasian population.

The ChinaMAP analytics of deep whole genome sequences in 10,588 individuals

Metabolic diseases are the most common and rapidly growing health issues worldwide. The massive population-based human genetics is crucial for the precise prevention and intervention of metabolic disorders. The China Metabolic Analytics Project (ChinaMAP) is based on cohort studies across diverse regions and ethnic groups with metabolic phenotypic data in China. Here, we describe the centralized analysis of the deep whole genome sequencing data and the genetic bases of metabolic traits in 10,588 individuals from the ChinaMAP. The frequency spectrum of variants, population structure, pathogenic variants and novel genomic characteristics were analyzed. The individual genetic evaluations of Mendelian diseases, nutrition and drug metabolism, and traits of blood glucose and BMI were integrated. Our study establishes a large-scale and deep resource for the genetics of East Asians and provides opportunities for novel genetic discoveries of metabolic characteristics and disorders.

Can the personalized medicine approach contribute in controlling tuberculosis in general and India in particular?

Poor drug compliance and drug-resistant Mycobacterium tuberculosis are the two principal obstacles in controlling tuberculosis (TB) in endemic regions including India, which has contributed the most to global TB burden. We argue here that a personalized medicine approach, to start with the N-acetyl transferase-2–isoniazid (NAT2–INH) model, could be a step forward in dealing with both these limitations in controlling TB in India.

Passenger Mutations in More Than 2,500 Cancer Genomes: Overall Molecular Functional Impact and Consequences

The dichotomous model of "drivers" and "passengers" in cancer posits that only a few mutations in a tumor strongly affect its progression, with the remaining ones being inconsequential. Here, we leveraged the comprehensive variant dataset from the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) project to demonstrate that-in addition to the dichotomy of high- and low-impact variants-there is a third group of medium-impact putative passengers. Moreover, we also found that molecular impact correlates with subclonal architecture (i.e., early versus late mutations), and different signatures encode for mutations with divergent impact. Furthermore, we adapted an additive-effects model from complex-trait studies to show that the aggregated effect of putative passengers, including undetected weak drivers, provides significant additional power (∼12% additive variance) for predicting cancerous phenotypes, beyond PCAWG-identified driver mutations. Finally, this framework allowed us to estimate the frequency of potential weak-driver mutations in PCAWG samples lacking any well-characterized driver alterations.

Evolutionary perspectives on polygenic selection, missing heritability, and GWAS

Genome-wide association studies (GWAS) have successfully identified many trait-associated variants, but there is still much we do not know about the genetic basis of complex traits. Here, we review recent theoretical and empirical literature regarding selection on complex traits to argue that "missing heritability" is as much an evolutionary problem as it is a statistical problem. We discuss empirical findings that suggest a role for selection in shaping the effect sizes and allele frequencies of causal variation underlying complex traits, and the limitations of these studies. We then use simulations of selection, realistic genome structure, and complex human demography to illustrate the results of recent theoretical work on polygenic selection, and show that statistical inference of causal loci is sharply affected by evolutionary processes. In particular, when selection acts on causal alleles, it hampers the ability to detect causal loci and constrains the transferability of GWAS results across populations. Last, we discuss the implications of these findings for future association studies, and suggest that future statistical methods to infer causal loci for genetic traits will benefit from explicit modeling of the joint distribution of effect sizes and allele frequencies under plausible evolutionary models.

Identification of trace metals and potential anthropogenic influences on the historic New York African Burial Ground population: A pXRF technology approach

The New York African Burial Ground (NYABG) is the country’s oldest and largest burial site of free and enslaved Africans. Re-discovered in 1991, this site provided evidence of the biological and cultural existence of a 17^th and 18^th Century historic population viewing their skeletal remains. However, the skeletal remains were reburied in October 2003 and are unavailable for further investigation. The analysis of grave soil samples with modern technology allows for the assessment of trace metal presence. Portable X-ray fluorescence (pXRF) spectrometry provides a semi-quantitative and non-destructive method to identify trace metals of this population and in the surrounding environment. Sixty-five NYABG soil samples were analyzed on a handheld Bruker Tracer III- SD XRF with 40 kV of voltage and a 30μA current. Presence of As, Cu, and Zn can potentially decipher the influence of the local 18^th Century pottery factories. Elevated levels of Sr validate the assumed heavy vegetative diets of poor and enslaved Africans of the time. Decreased levels of Ca may be due in part to the proximity of the Collect Pond, the existing water table until the early 19^th Century, and Manhattan’s rising sea level causing an elevated water table washing away the leached Ca from human remains. These data help us reconstruct the lives of these early Americans in what became New York City.

Light at Night Exposure Effects on Differentiation and Cell Cycle in the Rat Liver With Autonomic Nervous System Denervation

Introduction: N Exposure to artificial light at night (LAN) affects human health and causes several functional modifications in the body. Obesity, diabetes, and hormonal changes are reported after exposure to LAN in humans. This study aims to highlight the critical features of biological terms that are affected in the liver of rats which received autonomic nervous system denervation. Methods: The liver gene expression profiles of 8 male Wistar rats that received sympathetic plus parasympathetic hepatic denervation and were exposed to LAN from Gene Expression Omnibus (GEO) for 1 hour were compared with 5 controls. The significant differentially-expressed genes (DEGs) were screened by the protein-protein interaction (PPI) network analysis STRING database (an application of Cytoscape software). Also, CuleGO and CleuDedia, the 2 applications of Cytoscape software, were used for more analysis. Results: Among 250 DEGs, 173 characterized genes with fold change more than 2 plus 100 added relevant genes were included in the PPI network. The analysis of the main connected component (MCC) led to introducing 15 hubs and 15 bottlenecks. CCT2, COPS7A, KAT2A, and ERCC1 were determined as hub-bottlenecks. Among hubs and bottlenecks, DHX15, KAT2A, CCT2, HSP90AB1, CCNE1, DHX16, LSM2, WEE1, CWC27, BAZ1B, RAB22A, DNM2, and DHX30 were linked to each other by various kinds of actions. CCT2 and KAT2A, the 2 hub-bottlenecks, were included in the interacted genes in the action map. Four classes of biological terms including negative regulation of non-motile cilium assembly, negative regulation of transforming growth factor beta activation, alpha-tubulin acetylation, and histamine-induced gastric acid secretion were identified as the critical biochemical pathways and biological processes. Conclusion: Several essential functions such as differentiation, cell cycle, ribosome assembly, and splicing are affected by LAN in rat livers with autonomic nervous system denervation.

THE EXPOSOME IN HUMAN EVOLUTION: FROM DUST TO DIESEL

Global exposures to air pollution and cigarette smoke are novel in human evolutionary history and are associated with about 16 million premature deaths per year. We investigate the history of the human exposome for relationships between novel environmental toxins and genetic changes during human evolution in six phases. Phase I: With increased walking on savannas, early human ancestors inhaled crustal dust, fecal aerosols, and spores; carrion scavenging introduced new infectious pathogens. Phase II: Domestic fire exposed early Homo to novel toxins from smoke and cooking. Phases III and IV: Neolithic to preindustrial Homo sapiens incurred infectious pathogens from domestic animals and dense communities with limited sanitation. Phase V: Industrialization introduced novel toxins from fossil fuels, industrial chemicals, and tobacco at the same time infectious pathogens were diminishing. Thereby, pathogen-driven causes of mortality were replaced by chronic diseases driven by sterile inflammogens, exogenous and endogenous. Phase VI: Considers future health during global warming with increased air pollution and infections. We hypothesize that adaptation to some ancient toxins persists in genetic variations associated with inflammation and longevity.

PGG.SNV: understanding the evolutionary and medical implications of human single nucleotide variations in diverse populations

Despite the tremendous growth of the DNA sequencing data in the last decade, our understanding of the human genome is still in its infancy. To understand the implications of genetic variants in the light of population genetics and molecular evolution, we developed a database, PGG.SNV ( https://www.pggsnv.org ), which gives much higher weight to previously under-investigated indigenous populations in Asia. PGG.SNV archives 265 million SNVs across 220,147 present-day genomes and 1018 ancient genomes, including 1009 newly sequenced genomes, representing 977 global populations. Moreover, estimation of population genetic diversity and evolutionary parameters is available in PGG.SNV, a unique feature compared with other databases.

Future Preventive Gene Therapy of Polygenic Diseases from a Population Genetics Perspective

With the accumulation of scientific knowledge of the genetic causes of common diseases and continuous advancement of gene-editing technologies, gene therapies to prevent polygenic diseases may soon become possible. This study endeavored to assess population genetics consequences of such therapies. Computer simulations were used to evaluate the heterogeneity in causal alleles for polygenic diseases that could exist among geographically distinct populations. The results show that although heterogeneity would not be easily detectable by epidemiological studies following population admixture, even significant heterogeneity would not impede the outcomes of preventive gene therapies. Preventive gene therapies designed to correct causal alleles to a naturally-occurring neutral state of nucleotides would lower the prevalence of polygenic early- to middle-age-onset diseases in proportion to the decreased population relative risk attributable to the edited alleles. The outcome would manifest differently for late-onset diseases, for which the therapies would result in a delayed disease onset and decreased lifetime risk; however, the lifetime risk would increase again with prolonging population life expectancy, which is a likely consequence of such therapies. If the preventive heritable gene therapies were to be applied on a large scale, the decreasing frequency of risk alleles in populations would reduce the disease risk or delay the age of onset, even with a fraction of the population receiving such therapies. With ongoing population admixture, all groups would benefit over generations.